系統エンティティをクエリする

from sagemaker.lineage.context import Context, EndpointContext
from sagemaker.lineage.action import Action
from sagemaker.lineage.association import Association
from sagemaker.lineage.artifact import Artifact, ModelArtifact, DatasetArtifact

from sagemaker.lineage.query import (
    LineageQuery,
    LineageFilter,
    LineageSourceEnum,
    LineageEntityEnum,
    LineageQueryDirectionEnum,
)
# Find the endpoint context and model artifact that should be used for the lineage queries.

contexts = Context.list(source_uri=endpoint_arn)
context_name = list(contexts)[0].context_name
endpoint_context = EndpointContext.load(context_name=context_name)
        

# Define the LineageFilter to look for entities of type `ARTIFACT` and the source of type `DATASET`.

query_filter = LineageFilter(
    entities=[LineageEntityEnum.ARTIFACT], sources=[LineageSourceEnum.DATASET]
)

# Providing this `LineageFilter` to the `LineageQuery` constructs a query that traverses through the given context `endpoint_context`
# and find all datasets.

query_result = LineageQuery(sagemaker_session).query(
    start_arns=[endpoint_context.context_arn],
    query_filter=query_filter,
    direction=LineageQueryDirectionEnum.ASCENDANTS,
    include_edges=False,
)

# Parse through the query results to get the lineage objects corresponding to the datasets
dataset_artifacts = []
for vertex in query_result.vertices:
    dataset_artifacts.append(vertex.to_lineage_object().source.source_uri)

pp.pprint(dataset_artifacts)

# Define the LineageFilter to look for entities of type `ARTIFACT` and the source of type `MODEL`.

query_filter = LineageFilter(
    entities=[LineageEntityEnum.ARTIFACT], sources=[LineageSourceEnum.MODEL]
)

# Providing this `LineageFilter` to the `LineageQuery` constructs a query that traverses through the given context `endpoint_context`
# and find all datasets.

query_result = LineageQuery(sagemaker_session).query(
    start_arns=[endpoint_context.context_arn],
    query_filter=query_filter,
    direction=LineageQueryDirectionEnum.ASCENDANTS,
    include_edges=False,
)

# Parse through the query results to get the lineage objects corresponding to the model
model_artifacts = []
for vertex in query_result.vertices:
    model_artifacts.append(vertex.to_lineage_object().source.source_uri)

# The results of the `LineageQuery` API call return the ARN of the model deployed to the endpoint along with
# the S3 URI to the model.tar.gz file associated with the model
pp.pprint(model_artifacts)

# Define the LineageFilter to look for entities of type `TRIAL_COMPONENT` and the source of type `TRAINING_JOB`.

query_filter = LineageFilter(
    entities=[LineageEntityEnum.TRIAL_COMPONENT],
    sources=[LineageSourceEnum.TRAINING_JOB],
)

# Providing this `LineageFilter` to the `LineageQuery` constructs a query that traverses through the given context `endpoint_context`
# and find all datasets.

query_result = LineageQuery(sagemaker_session).query(
    start_arns=[endpoint_context.context_arn],
    query_filter=query_filter,
    direction=LineageQueryDirectionEnum.ASCENDANTS,
    include_edges=False,
)

# Parse through the query results to get the ARNs of the training jobs associated with this Endpoint
trial_components = []
for vertex in query_result.vertices:
    trial_components.append(vertex.arn)

pp.pprint(trial_components)

LineageQuery を変更して異なる start_arns を持たせると、系統の焦点を変更できます。また、LineageFilter で複数のソースとエンティティを取れば、クエリの範囲を拡大できます。

以下では、モデルを系統の焦点として使用し、それに関連付けられているエンドポイントとデータセットを見つけます。

# Get the ModelArtifact

model_artifact_summary = list(Artifact.list(source_uri=model_package_arn))[0]
model_artifact = ModelArtifact.load(artifact_arn=model_artifact_summary.artifact_arn)
query_filter = LineageFilter(
    entities=[LineageEntityEnum.ARTIFACT],
    sources=[LineageSourceEnum.ENDPOINT, LineageSourceEnum.DATASET],
)

query_result = LineageQuery(sagemaker_session).query(
    start_arns=[model_artifact.artifact_arn],  # Model is the starting artifact
    query_filter=query_filter,
    # Find all the entities that descend from the model, i.e. the endpoint
    direction=LineageQueryDirectionEnum.DESCENDANTS,
    include_edges=False,
)

associations = []
for vertex in query_result.vertices:
    associations.append(vertex.to_lineage_object().source.source_uri)

query_result = LineageQuery(sagemaker_session).query(
    start_arns=[model_artifact.artifact_arn],  # Model is the starting artifact
    query_filter=query_filter,
    # Find all the entities that ascend from the model, i.e. the datasets
    direction=LineageQueryDirectionEnum.ASCENDANTS,
    include_edges=False,
)

for vertex in query_result.vertices:
    associations.append(vertex.to_lineage_object().source.source_uri)

pp.pprint(associations)

方向が BOTH に設定されている場合、クエリはグラフを横断して、祖先と子孫の関係を見つけます。この横断は、開始ノードだけでなく、訪問先の各ノードから行われます。例えば、トレーニングジョブが 2 回実行され、トレーニングジョブによって生成されたモデルの両方がエンドポイントにデプロイされている場合、方向が BOTH に設定されたクエリの結果には両方のエンドポイントが示されます。これは、モデルのトレーニングとデプロイに同じイメージが使用されているためです。イメージはモデルと共通であるため、start_arn と両方のエンドポイントがクエリ結果に表示されます。

query_filter = LineageFilter(
    entities=[LineageEntityEnum.ARTIFACT],
    sources=[LineageSourceEnum.ENDPOINT, LineageSourceEnum.DATASET],
)

query_result = LineageQuery(sagemaker_session).query(
    start_arns=[model_artifact.artifact_arn],  # Model is the starting artifact
    query_filter=query_filter,
    # This specifies that the query should look for associations both ascending and descending for the start
    direction=LineageQueryDirectionEnum.BOTH,
    include_edges=False,
)

associations = []
for vertex in query_result.vertices:
    associations.append(vertex.to_lineage_object().source.source_uri)

pp.pprint(associations)

系統グラフの方向を理解するために、次のエンティティ関係グラフを取得します ([Dataset] (データセット) -> [Training Job] (トレーニングジョブ) -> [Model] (モデル) -> [Endpoint] (エンドポイント))。

エンドポイントはモデルの子孫であり、モデルはデータセットの子孫です。同様に、モデルはエンドポイントの祖先です。direction パラメータを使用すると、start_arns のエンティティの子孫または祖先のエンティティをクエリで返すかどうかを指定できます。start_arns にモデルが含まれており、方向が DESCENDANTS の場合は、クエリはエンドポイントを返します。方向が ASCENDANTS の場合、クエリはデータセットを返します。

# In this example, we'll look at the impact of specifying the direction as ASCENDANT or DESCENDANT in a `LineageQuery`.

query_filter = LineageFilter(
    entities=[LineageEntityEnum.ARTIFACT],
    sources=[
        LineageSourceEnum.ENDPOINT,
        LineageSourceEnum.MODEL,
        LineageSourceEnum.DATASET,
        LineageSourceEnum.TRAINING_JOB,
    ],
)

query_result = LineageQuery(sagemaker_session).query(
    start_arns=[model_artifact.artifact_arn],
    query_filter=query_filter,
    direction=LineageQueryDirectionEnum.ASCENDANTS,
    include_edges=False,
)

ascendant_artifacts = []

# The lineage entity returned for the Training Job is a TrialComponent which can't be converted to a
# lineage object using the method `to_lineage_object()` so we extract the TrialComponent ARN.
for vertex in query_result.vertices:
    try:
        ascendant_artifacts.append(vertex.to_lineage_object().source.source_uri)
    except:
        ascendant_artifacts.append(vertex.arn)

print("Ascendant artifacts : ")
pp.pprint(ascendant_artifacts)

query_result = LineageQuery(sagemaker_session).query(
    start_arns=[model_artifact.artifact_arn],
    query_filter=query_filter,
    direction=LineageQueryDirectionEnum.DESCENDANTS,
    include_edges=False,
)

descendant_artifacts = []
for vertex in query_result.vertices:
    try:
        descendant_artifacts.append(vertex.to_lineage_object().source.source_uri)
    except:
        # Handling TrialComponents.
        descendant_artifacts.append(vertex.arn)

print("Descendant artifacts : ")
pp.pprint(descendant_artifacts)

クラスEndpointContext、ModelArtifact、DatasetArtifact には、LineageQuery API のラッパーであるヘルパー関数が備わっており、特定の系統クエリが活用しやすくなっています。以下の例は、これらのヘルパー関数の使用方法を示しています。

# Find all the datasets associated with this endpoint

datasets = []
dataset_artifacts = endpoint_context.dataset_artifacts()
for dataset in dataset_artifacts:
    datasets.append(dataset.source.source_uri)
print("Datasets : ", datasets)

# Find the training jobs associated with the endpoint
training_job_artifacts = endpoint_context.training_job_arns()
training_jobs = []
for training_job in training_job_artifacts:
    training_jobs.append(training_job)
print("Training Jobs : ", training_jobs)

# Get the ARN for the pipeline execution associated with this endpoint (if any)
pipeline_executions = endpoint_context.pipeline_execution_arn()
if pipeline_executions:
    for pipeline in pipelines_executions:
        print(pipeline)

# Here we use the `ModelArtifact` class to find all the datasets and endpoints associated with the model

dataset_artifacts = model_artifact.dataset_artifacts()
endpoint_contexts = model_artifact.endpoint_contexts()

datasets = [dataset.source.source_uri for dataset in dataset_artifacts]
endpoints = [endpoint.source.source_uri for endpoint in endpoint_contexts]

print("Datasets associated with this model : ")
pp.pprint(datasets)

print("Endpoints associated with this model : ")
pp.pprint(endpoints)

# Here we use the `DatasetArtifact` class to find all the endpoints hosting models that were trained with a particular dataset
# Find the artifact associated with the dataset

dataset_artifact_arn = list(Artifact.list(source_uri=training_data))[0].artifact_arn
dataset_artifact = DatasetArtifact.load(artifact_arn=dataset_artifact_arn)

# Find the endpoints that used this training dataset
endpoint_contexts = dataset_artifact.endpoint_contexts()
endpoints = [endpoint.source.source_uri for endpoint in endpoint_contexts]

print("Endpoints associated with the training dataset {}".format(training_data))
pp.pprint(endpoints)

ヘルパークラス Visualizer は、サンプルノートブック visualizer.py で提供され、系統グラフのプロットをサポートします。クエリレスポンスがレンダリングされると、StartArns の系統関係を合わせたグラフが表示されます。StartArns からの視覚化には、query_lineage API アクションで返されたその他の系統エンティティとの関係が示されます。

# Graph APIs
# Here we use the boto3 `query_lineage` API to generate the query response to plot.

from visualizer import Visualizer

query_response = sm_client.query_lineage(
    StartArns=[endpoint_context.context_arn], Direction="Ascendants", IncludeEdges=True
)

viz = Visualizer()
viz.render(query_response, "Endpoint")
        
        query_response = sm_client.query_lineage(
    StartArns=[model_artifact.artifact_arn], Direction="Ascendants", IncludeEdges=True
)
viz.render(query_response, "Model")